System and methods for sandboxed malware analysis and automated patch development, deployment and validation

ABSTRACT

A system and methods for sandboxed malware analysis and automated patch development, deployment and validation, comprising a business operating system, vulnerability scoring engine, binary translation engine, sandbox simulation engine, at least one network endpoint, at least one database, a network, and a combination of machine learning and vulnerability probing techniques, to analyze software, locate any vulnerabilities or malicious behavior, and attempt to patch and prevent undesired behavior from occurring, autonomously.

CROSS-REFERENCE TO RELATED APPLICATIONS

Application No. Date Filed Tide Current Herewith A SYSTEM AND METHODSFOR application SANDBOXED MALWARE ANALYSIS AND AUTOMATED PATCHDEVELOPMENT, DEPLOYMENT AND VALIDATION Is a continuation of: 15/887,496Feb. 2, 2018 SYSTEM AND METHODS FOR SANDBOXED MALWARE ANALYSIS ANDAUTOMATED PATCH DEVELOPMENT, DEPLOYMENT AND VALIDATION which is acontinuation-in-part of: 15/818,733 Nov. 20, 2017 SYSTEM AND METHOD FORPat. No. Issue Date CYBERSECURITY ANALYSIS AND 10,673,887 Jun. 2, 2020SCORE GENERATION FOR INSURANCE PURPOSES which is a continuation-in-partof: 15/725,274 Oct. 4, 2017 APPLICATION OF ADVANCED Pat. No. Issue DateCYBERSECURITY THREAT 10,609,079 Mar. 31, 2020 MITIGATION TO ROGUEDEVICES, PRIVILEGE ESCALATION, AND RISK-BASED VULNERABILITY AND PATCHMANAGEMENT which is a continuation-in-part of: 15/655,113 Jul. 20, 2017ADVANCED CYBERSECURITY Pat. No. Issue Date THREAT MITIGATION 10,735,456Aug. 4, 2020 USING BEHAVIORAL AND DEEP ANALYTICS which is acontinuation-in-part of: 15/616,427 Jun. 7, 2017 RAPID PREDICTIVEANALYSIS OF VERY LARGE DATA SETS USING AN ACTOR-DRIVEN DISTRIBUTEDCOMPUTATIONAL GRAPH and is also a continuation-in-part of: 15/237,625Aug. 15, 2016 DETECTION MITIGATION AND Pat. No. Issue Date REMEDIATIONOF CYBERATTACKS 10,248,910 Apr. 2, 2019 EMPLOYING AN ADVANCED CYBER-DECISION PLATFORM which is a continuation-in-part of: 15/206,195 Jul. 8,2016 ACCURATE AND DETAILED MODELING OF SYSTEMS WITH LARGE COMPLEXDATASETS USING A DISTRIBUTED SIMULATION ENGINE which is acontinuation-in-part of: 15/186,453 Jun. 18, 2016 SYSTEM FOR AUTOMATEDCAPTURE AND ANALYSIS OF BUSINESS INFORMATION FOR RELIABLE BUSINESSVENTURE OUTCOME PREDICTION which is a continuation-in-part of:15/166,158 May 26, 2016 SYSTEM FOR AUTOMATED CAPTURE AND ANALYSIS OFBUSINESS INFORMATION FOR SECURITY AND CLIENT-FACING INFRASTRUCTURERELIABILITY which is a continuation-in-part of: 15/141,752 Apr. 28, 2016SYSTEM FOR FULLY INTEGRATED CAPTURE, AND ANALYSIS OF BUSINESSINFORMATION RESULTING IN PREDICTIVE DECISION MAKING AND SIMULATION whichis a continuation-in-part of: 15/091,563 Apr. 5, 2016 SYSTEM FORCAPTURE, ANALYSIS Pat. No. Issue Date AND STORAGE OF TIME SERIES10,204,147 Feb. 12, 2019 DATA FROM SENSORS WITH HETEROGENEOUS REPORTINTERVAL PROFILES and is also a continuation-in-part of: 14/986,536 Dec.31, 2015 DISTRIBUTED SYSTEM FOR LARGE Pat. No. Issue Date VOLUME DEEPWEB DATA 10,210,255 Feb. 19, 2019 EXTRACTION and is also acontinuation-in-part of: 14/925,974 Oct. 28, 2015 RAPID PREDICTIVEANALYSIS OF VERY LARGE DATA SETS USING THE DISTRIBUTED COMPUTATIONALGRAPH Current Herewith A SYSTEM AND METHODS FOR application SANDBOXEDMALWARE ANALYSIS AND AUTOMATED PATCH DEVELOPMENT, DEPLOYMENT ANDVALIDATION Is a continuation of: 15/887,496 Feb. 2, 2018 SYSTEM ANDMETHODS FOR SANDBOXED MALWARE ANALYSIS AND AUTOMATED PATCH DEVELOPMENT,DEPLOYMENT AND VALIDATION which is a continuation-in-part of: 15/823,285Nov. 27, 2017 META-INDEXING, SEARCH, Pat. No. Issue Date COMPLIANCE, AND10,740,096 Aug. 11, 2020 TEST FRAMEWORK FOR SOFTWARE DEVELOPMENT whichis a continuation-in-part of: 15/788,718 Oct. 19, 2017 DATA MONETIZATIONAND EXCHANGE PLATFORM which claims priority, and benefit to: 62/568,307Oct. 4, 2017 DATA MONETIZATION AND EXCHANGE PLATFORM and is also acontinuation-in-part of: 15/788,002 Oct. 19, 2017 ALGORITHM MONETIZATIONAND EXCHANGE PLATFORM which claims priority, and benefit to: 62/568,305Oct. 4, 2017 ALGORITHM MONETIZATION AND EXCHANGE PLATFORM and is also acontinuation-in-part of: 15/787,601 Oct. 18, 2017 METHOD AND APPARATUSFOR CROWDSOURCED DATA GATHERING, EXTRACTION, AND COMPENSATION whichclaims priority, and benefit to: 62/568,312 Oct. 4, 2017 METHOD ANDAPPARATUS FOR CROWDSOURCED DATA GATHERING, EXTRACTION, AND COMPENSATIONand is also a continuation-in-part of: 15/616,427 Jun. 7, 2017 RAPIDPREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING AN ACTOR-DRIVENDISTRIBUTED COMPUTATIONAL GRAPH which is a continuation-in-part of:14/925,974 Oct. 28, 2015 RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATASETS USING THE DISTRIBUTED COMPUTATIONAL GRAPHY the entire specificationof each of which is incorporated herein by reference.

BACKGROUND OF THE INVENTION Field of the Art

The disclosure relates to the field of computer management, and moreparticularly to the field of cybersecurity and threat analytics.

Discussion of the State of the Art

On Aug. 4, 2016, United States government's DEFENSE ADVANCED RESEARCHPROJECTS AGENCY (DARPA)™ hosted an event in 2016 called the Cyber GrandChallenge, aimed at creating an automatic defense system for networkdefense and vulnerability detection and patching. During the eventnumerous teams and individuals competed to develop a system which couldautomatically detect vulnerabilities and exploits in software systems,develop a patch, and deploy the patch within a finite amount of time, inan effort to produce a highly robust system to defend software systemsfrom a variety of possible exploits and malicious attacks. Thecompetition was partially successful, with the submitted systems fromeach team competing automatically in a capture-the-flag stylecompetition, and the competition in its entirety demonstrated that fullyautonomous network defense and exploitation is possible. No team'ssubmission completed the competition with 100% success in identifyingvulnerabilities and exploits, and as of yet no such system is deployedfor large scale or commercial applications in automated analysis anddefense of networks and network-connected devices. Malware of today iscontinually being advanced in the area of memory scanning, to evadedetection from current anti-virus and antimalware software, andcontinually advancing and evolving network and system defense techniquesare required in order to keep up with the pace of advancement of malwareboth today and in the future. Even until this competition, no systemexisted even for research applications which could reliably identify andpatch vulnerabilities and exploits in software systems and networksbefore malware took advantage of said vulnerabilities in the software.It is commonly the case that vulnerabilities and exploits in softwareare only found out and then patched some time after they are takenadvantage of, falling out of view of the system developers before theissue is made use of by malicious actors, for example the Heartbleedexploit present in many OpenSSL systems until patched in 2014, onlyshortly after the vulnerability was publicly disclosed.

What is needed is a system and methods for sandboxed malware analysisand automated patch development, deployment and validation, and further,a system which can use state-of-the-art machine learning techniques andartificial intelligence paradigms to evolve its understanding of malwareanalysis to help keep pace with the advancement of malware in the world.

SUMMARY OF THE INVENTION

Accordingly, the inventor has conceived and reduced to practice, in apreferred embodiment of the invention, a system and methods forsandboxed malware analysis and automated patch development, deploymentand validation. The following non-limiting summary of the invention isprovided for clarity, and should be construed consistently withembodiments described in the detailed description below.

To solve the problem of malware advancing beyond the capabilities ofexisting antimalware capabilities, a system and methods have beendevised comprising a specialized business operating system, the abilityto convert files into binary to be executed in a sandbox environment,machine learning capabilities, a cybersecurity scoring system, patternmatching heuristics, in a system designed to find vulnerabilitiespresent in networks and files on a computer system, analyze thevulnerabilities and exploits present, develop software patches for thevulnerabilities and exploits, and deploy the software patchesautonomously, as well as learn from and evolve according to present andemerging malware techniques using machine learning techniques.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawings illustrate several aspects and, together withthe description, serve to explain the principles of the inventionaccording to the aspects. It will be appreciated by one skilled in theart that the particular arrangements illustrated in the drawings aremerely exemplary, and are not to be considered as limiting of the scopeof the invention or the claims herein in any way.

FIG. 1 is a diagram of an exemplary architecture of a system for thecapture and storage of time series data from sensors with heterogeneousreporting profiles according to a preferred aspect of the invention.

FIG. 2 is a diagram of an exemplary architecture of a business operatingsystem according to a preferred aspect of the invention.

FIG. 3 is a diagram of an exemplary architecture of a cybersecurityanalysis system according to a preferred aspect of the invention.

FIG. 4 is a system diagram illustrating connections between importantcomponents for analyzing software and network-connected endpoints,according to a preferred aspect.

FIG. 5 is a method diagram illustrating important steps in detecting andanalyzing software exploits or vulnerabilities, according to a preferredaspect of the invention.

FIG. 6 is a method diagram illustrating the use of advanced endpointinstrumentation to collect data on endpoint devices across a network,according to a preferred aspect.

FIG. 7 is a method diagram illustrating the prioritization of softwareflaws and exploits according to a preferred aspect.

FIG. 8 is a method diagram illustrating the basic steps for patchingexploits and vulnerabilities in analyzed software, according to anaspect.

FIG. 9 is a block diagram illustrating an exemplary hardwarearchitecture of a computing device.

FIG. 10 is a block diagram illustrating an exemplary logicalarchitecture for a client device.

FIG. 11 is a block diagram showing an exemplary architecturalarrangement of clients, servers, and external services.

FIG. 12 is another block diagram illustrating an exemplary hardwarearchitecture of a computing device.

DETAILED DESCRIPTION

The inventor has conceived, and reduced to practice, a system andmethods for sandboxed malware analysis and automated patch development,deployment and validation.

One or more different aspects may be described in the presentapplication. Further, for one or more of the aspects described herein,numerous alternative arrangements may be described; it should beappreciated that these are presented for illustrative purposes only andare not limiting of the aspects contained herein or the claims presentedherein in any way. One or more of the arrangements may be widelyapplicable to numerous aspects, as may be readily apparent from thedisclosure. In general, arrangements are described in sufficient detailto enable those skilled in the art to practice one or more of theaspects, and it should be appreciated that other arrangements may beutilized and that structural, logical, software, electrical and otherchanges may be made without departing from the scope of the particularaspects. Particular features of one or more of the aspects describedherein may be described with reference to one or more particular aspectsor figures that form a part of the present disclosure, and in which areshown, by way of illustration, specific arrangements of one or more ofthe aspects. It should be appreciated, however, that such features arenot limited to usage in the one or more particular aspects or figureswith reference to which they are described. The present disclosure isneither a literal description of all arrangements of one or more of theaspects nor a listing of features of one or more of the aspects thatmust be present in all arrangements.

Headings of sections provided in this patent application and the titleof this patent application are for convenience only, and are not to betaken as limiting the disclosure in any way.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or morecommunication means or intermediaries, logical or physical.

A description of an aspect with several components in communication witheach other does not imply that all such components are required. To thecontrary, a variety of optional components may be described toillustrate a wide variety of possible aspects and in order to more fullyillustrate one or more aspects. Similarly, although process steps,method steps, algorithms or the like may be described in a sequentialorder, such processes, methods and algorithms may generally beconfigured to work in alternate orders, unless specifically stated tothe contrary. In other words, any sequence or order of steps that may bedescribed in this patent application does not, in and of itself,indicate a requirement that the steps be performed in that order. Thesteps of described processes may be performed in any order practical.Further, some steps may be performed simultaneously despite beingdescribed or implied as occurring non-simultaneously (e.g., because onestep is described after the other step). Moreover, the illustration of aprocess by its depiction in a drawing does not imply that theillustrated process is exclusive of other variations and modificationsthereto, does not imply that the illustrated process or any of its stepsare necessary to one or more of the aspects, and does not imply that theillustrated process is preferred. Also, steps are generally describedonce per aspect, but this does not mean they must occur once, or thatthey may only occur once each time a process, method, or algorithm iscarried out or executed. Some steps may be omitted in some aspects orsome occurrences, or some steps may be executed more than once in agiven aspect or occurrence.

When a single device or article is described herein, it will be readilyapparent that more than one device or article may be used in place of asingle device or article. Similarly, where more than one device orarticle is described herein, it will be readily apparent that a singledevice or article may be used in place of the more than one device orarticle.

The functionality or the features of a device may be alternativelyembodied by one or more other devices that are not explicitly describedas having such functionality or features. Thus, other aspects need notinclude the device itself.

Techniques and mechanisms described or referenced herein will sometimesbe described in singular form for clarity. However, it should beappreciated that particular aspects may include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. Process descriptions or blocks in figures should beunderstood as representing modules, segments, or portions of code whichinclude one or more executable instructions for implementing specificlogical functions or steps in the process. Alternate implementations areincluded within the scope of various aspects in which, for example,functions may be executed out of order from that shown or discussed,including substantially concurrently or in reverse order, depending onthe functionality involved, as would be understood by those havingordinary skill in the art.

Definitions

As used herein, a “swimlane” is a communication channel between a timeseries sensor data reception and apportioning device and a data storemeant to hold the apportioned data time series sensor data. A swimlaneis able to move a specific, finite amount of data between the twodevices. For example a single swimlane might reliably carry and haveincorporated into the data store, the data equivalent of 5 seconds worthof data from 10 sensors in 5 seconds, this being its capacity. Attemptsto place 5 seconds worth of data received from 6 sensors using oneswimlane would result in data loss.

As used herein, a “metaswimlane” is an as-needed logical combination oftransfer capacity of two or more real swimlanes that is transparent tothe requesting process. Sensor studies where the amount of data receivedper unit time is expected to be highly heterogeneous over time may beinitiated to use metaswimlanes. Using the example used above that asingle real swimlane can transfer and incorporate the 5 seconds worth ofdata of 10 sensors without data loss, the sudden receipt of incomingsensor data from 13 sensors during a 5 second interval would cause thesystem to create a two swimlane metaswimlane to accommodate the standard10 sensors of data in one real swimlane and the 3 sensor data overage inthe second, transparently added real swimlane, however no changes to thedata receipt logic would be needed as the data reception andapportionment device would add the additional real swimlanetransparently.

Conceptual Architecture

FIG. 1 is a diagram of an exemplary architecture of a system for thecapture and storage of time series data from sensors with heterogeneousreporting profiles according to a preferred aspect of the invention 100.In this embodiment, a plurality of sensor devices 110 a-n stream data toa collection device, in this case a web server acting as a networkgateway 115. These sensors 110 a-n can be of several forms, somenon-exhaustive examples being: physical sensors measuring humidity,pressure, temperature, orientation, and presence of a gas; or virtualsuch as programming measuring a level of network traffic, memory usagein a controller, and number of times the word “refill” is used in astream of email messages on a particular network segment, to name asmall few of the many diverse forms known to the art. In the embodiment,the sensor data is passed without transformation to the data managementengine 120, where it is aggregated and organized for storage in aspecific type of data store 125 designed to handle the multidimensionaltime series data resultant from sensor data. Raw sensor data can exhibithighly different delivery characteristics. Some sensor sets may deliverlow to moderate volumes of data continuously. It would be infeasible toattempt to store the data in this continuous fashion to a data store asattempting to assign identifying keys and store real time data frommultiple sensors would invariably lead to significant data loss. In thiscircumstance, the data stream management engine 120 would hold incomingdata in memory, keeping only the parameters, or “dimensions” from withinthe larger sensor stream that are pre-decided by the administrator ofthe study as important and instructions to store them transmitted fromthe administration device 112. The data stream management engine 120would then aggregate the data from multiple individual sensors andapportion that data at a predetermined interval, for example, every 10seconds, using the timestamp as the key when storing the data to amultidimensional time series data store over a single swimlane ofsufficient size. This highly ordered delivery of a foreseeable amount ofdata per unit time is particularly amenable to data capture and storagebut patterns where delivery of data from sensors occurs irregularly andthe amount of data is extremely heterogeneous are quite prevalent. Inthese situations, the data stream management engine cannot successfullyuse strictly single time interval over a single swimlane mode of datastorage. In addition to the single time interval method the inventionalso can make use of event based storage triggers where a predeterminednumber of data receipt events, as set at the administration device 112,triggers transfer of a data block consisting of the apportioned numberof events as one dimension and a number of sensor ids as the other. Inthe embodiment, the system time at commitment or a time stamp that ispart of the sensor data received is used as the key for the data blockvalue of the value-key pair. The invention can also accept a raw datastream with commitment occurring when the accumulated stream datareaches a predesigned size set at the administration device 112.

It is also likely that that during times of heavy reporting from amoderate to large array of sensors, the instantaneous load of data to becommitted will exceed what can be reliably transferred over a singleswimlane. The embodiment of the invention can, if capture parameterspre-set at the administration device 112, combine the data movementcapacity of two or more swimlanes, the combined bandwidth dubbed ametaswimlane, transparently to the committing process, to accommodatethe influx of data in need of commitment. All sensor data, regardless ofdelivery circumstances are stored in a multidimensional time series datastore 125 which is designed for very low overhead and rapid data storageand minimal maintenance needs to sap resources. The embodiment uses akey-value pair data store examples of which are Riak, Redis and BerkeleyDB for their low overhead and speed, although the invention is notspecifically tied to a single data store type to the exclusion of othersknown in the art should another data store with better response andfeature characteristics emerge. Due to factors easily surmised by thoseknowledgeable in the art, data store commitment reliability is dependenton data store data size under the conditions intrinsic to time seriessensor data analysis. The number of data records must be kept relativelylow for the herein disclosed purpose. As an example one group ofdevelopers restrict the size of their multidimensional time serieskey-value pair data store to approximately 8.64×10⁴ records, equivalentto 24 hours of 1 second interval sensor readings or 60 days of 1 minuteinterval readings. In this development system the oldest data is deletedfrom the data store and lost. This loss of data is acceptable underdevelopment conditions but in a production environment, the loss of theolder data is almost always significant and unacceptable. The inventionaccounts for this need to retain older data by stipulating that ageddata be placed in long term storage. In the embodiment, the archivalstorage is included 130. This archival storage might be locally providedby the user, might be cloud based such as that offered by Amazon WebServices or Google or could be any other available very large capacitystorage method known to those skilled in the art.

Reliably capturing and storing sensor data as well as providing forlonger term, offline, storage of the data, while important, is only anexercise without methods to repetitively retrieve and analyze mostlikely differing but specific sets of data over time. The inventionprovides for this requirement with a robust query language that bothprovides straightforward language to retrieve data sets bounded bymultiple parameters, but to then invoke several transformations on thatdata set prior to output. In the embodiment isolation of desired datasets and transformations applied to that data occurs using pre-definedquery commands issued from the administration device 112 and acted uponwithin the database by the structured query interpreter 135. Below is ahighly simplified example statement to illustrate the method by which avery small number of options that are available using the structuredquery interpreter 135 might be accessed.

SELECT [STREAMING|EVENTS] data_spec FROM [unit] timestamp TO timestampGROUPBY (sensor_id, identifier) FILTER [filter_identifier] FORMAT[sensor [AS identifier] [, sensor [AS identifier]] . . . ](TEXT|JSON|FUNNEL|KML|GEOJSON|TOPOJSON);

Here “data_spec” might be replaced by a list of individual sensors froma larger array of sensors and each sensor in the list might be given ahuman readable identifier in the format “sensor AS identifier”. “unit”allows the researcher to assign a periodicity for the sensor data suchas second (s), minute (m), hour (h). One or more transformationalfilters, which include but a not limited to: mean, median, variance,standard deviation, standard linear interpolation, or Kalman filteringand smoothing, may be applied and then data formatted in one or moreformats examples of with are text, JSON, KML, GEOJSON and TOPOJSON amongothers known to the art, depending on the intended use of the data.

FIG. 2 is a diagram of an exemplary architecture of a business operatingsystem 200 according to a preferred aspect. Client access to the system205 both for system control and for interaction with system output suchas automated predictive decision making and planning and alternatepathway simulations, occurs through the system's highly distributed,very high bandwidth cloud interface 210 which is application driventhrough the use of the Scala/Lift development environment and webinteraction operation mediated by AWS ELASTIC BEANSTALK™, both used forstandards compliance and ease of development. Much of the business dataanalyzed by the system both from sources within the confines of theclient business, and from cloud based sources, also enter the systemthrough the cloud interface 210, data being passed to the analysis andtransformation components of the system, the directed computationalgraph module 255, high volume web crawling module 215 andmultidimensional time series database 220. The directed computationalgraph retrieves one or more streams of data from a plurality of sources,which includes, but is in no way not limited to, a number of physicalsensors, web based questionnaires and surveys, monitoring of electronicinfrastructure, crowd sourcing campaigns, and human input deviceinformation. Within the directed computational graph, data may be splitinto two identical streams, wherein one sub-stream may be sent for batchprocessing and storage while the other sub-stream may be reformatted fortransformation pipeline analysis. The data is then transferred togeneral transformer service 260 for linear data transformation as partof analysis or decomposable transformer service 250 for branching oriterative transformations that are part of analysis. The directedcomputational graph 255 represents all data as directed graphs where thetransformations are nodes and the result messages betweentransformations edges of the graph. These graphs which containconsiderable intermediate transformation data are stored and furtheranalyzed within graph stack module 245. High volume web crawling module215 uses multiple server hosted preprogrammed web spiders to find andretrieve data of interest from web based sources that are not welltagged by conventional web crawling technology. Multiple dimension timeseries database module 220 receives data from a large plurality ofsensors that may be of several different types. The module is designedto accommodate irregular and high volume surges by dynamically allottingnetwork bandwidth and server processing channels to process the incomingdata. Data retrieved by the multidimensional time series database 220and the high volume web crawling module 215 may be further analyzed andtransformed into task optimized results by the directed computationalgraph 255 and associated general transformer service 250 anddecomposable transformer service 260 modules.

Results of the transformative analysis process may then be combined withfurther client directives, additional business rules and practicesrelevant to the analysis and situational information external to thealready available data in the automated planning service module 230which also runs powerful predictive statistics functions and machinelearning algorithms to allow future trends and outcomes to be rapidlyforecast based upon the current system derived results and choosing eacha plurality of possible business decisions. Using all available data,the automated planning service module 230 may propose business decisionsmost likely to result is the most favorable business outcome with ausably high level of certainty. Closely related to the automatedplanning service module in the use of system derived results inconjunction with possible externally supplied additional information inthe assistance of end user business decision making, the businessoutcome simulation module 225 coupled with the end user facingobservation and state estimation service 240 allows business decisionmakers to investigate the probable outcomes of choosing one pendingcourse of action over another based upon analysis of the currentavailable data. For example, the pipelines operations department hasreported a very small reduction in crude oil pressure in a section ofpipeline in a highly remote section of territory. Many believe the issueis entirely due to a fouled, possibly failing flow sensor, othersbelieve that it is a proximal upstream pump that may have foreignmaterial stuck in it. Correction of both of these possibilities is toincrease the output of the effected pump to hopefully clean out it orthe fouled sensor. A failing sensor will have to be replaced at the nextmaintenance cycle. A few, however, feel that the pressure drop is due toa break in the pipeline, probably small at this point, but even so,crude oil is leaking and the remedy for the fouled sensor or pump optioncould make the leak much worse and waste much time afterwards. Thecompany does have a contractor about 8 hours away, or could rentsatellite time to look but both of those are expensive for a probablesensor issue, significantly less than cleaning up an oil spill thoughand then with significant negative public exposure. These sensor issueshave happened before and the business operating system 200 has data fromthem, which no one really studied due to the great volume of columnarfigures, so the alternative courses 225, 240 of action are run. Thesystem, based on all available data predicts that the fouled sensor orpump are unlikely the root cause this time due to other available dataand the contractor is dispatched. She finds a small breach in thepipeline. There will be a small cleanup and the pipeline needs to beshutdown for repair but multiple tens of millions of dollars have beensaved. This is just one example of a great many of the possible use ofthe business operating system, those knowledgeable in the art willeasily formulate more.

FIG. 3 is a system diagram, illustrating the connections between crucialcomponents, according to an aspect of the invention. Core componentsinclude a scheduling task engine 310 which will run any processes andcontinue with any steps desired by the client, as described in furthermethods and diagrams in the disclosure. Tasks may be scheduled to run atspecific times, or run for certain given amounts of time, which iscommonplace for task scheduling software and systems in the art. Thistask engine 310 is then connected to the internet, and possibly to asingle or plurality of local Multi-Dimensional Time-Series Databases(MDTSDB) 125. It is also possible to be connected to remotely hosted andcontrolled MDTSDB's 125 through the Internet, the physical location orproximity of the MDTSDB for this disclosure not being a limiting factor.In such cases as the MDTSDB 125 is not hosted locally, it must alsomaintain a connection to the Internet or another form of network forcommunication with the task engine 310. Device endpoints 330, especiallyInternet-of-Things (IoT) devices, are also by definition connected tothe internet, and in methods described in later figures will be used forcybersecurity analysis and risk assessment. The task engine 310 whichwill perform the scheduling and running of the methods described hereinalso maintains a connection to the scoring engine 320, which will beused to evaluate data gathered from the analysis and reconnaissancetasks run by the task scheduling engine 310.

FIG. 4 is a system diagram illustrating connections between importantcomponents for analyzing software and network-connected endpoints,according to a preferred aspect. A business operating system (OS) 410operates on a system outlined in FIG. 2 , with key components includingthe OS kernel 411 which is a component common to all operating systems,and on that kernel, aside from other pieces of software for otherpurposes, are two important engines, a binary file translator 412 and asandbox simulation environment 413. A binary file translator 412 mayconvert any given file or set of input data into executable machinecode, and a sandbox environment 413 is a simulation environment whichmay execute machine code in a closed-off environment, similar in purposeto an experiment carried out in a sealed room. This may be done in avariety of ways, including emulator software for specific systemarchitectures and open source code executors. Such an OS 410 would beconnected to a database 420, which may be formatted in Structured QueryLanguage (SQL) form, formatted raw text, encrypted text, or noSQL forms,and may be used in conjunction with management software such as APACHEHADOOP™ for increased performance. This connection may be either adirect physical connection, or the OS 410 and database 420 may belocated on the same physical machine, or they may be connected over anetwork 430, including the Internet or other kinds of computer networks.Similarly, a score generation system 440 may be connected to theoperating system 410, either through a network 430, or through a directphysical connection, or by operating on the same machine as theoperating system 410 itself. This scoring engine is used in laterfigures to prioritize software vulnerabilities and exploits. A varietyof device endpoints 450 may be connected over a network 430 andaccessible to the operating system 410, by use of endpointinstrumentation such as OSQUERY™, and these device endpoints may bevaried in form, including laptop computers, desktops, mobile phones, andvarious Internet of Things (IoT) devices. It is possible for only oneendpoint 451 to be connected, and it is similarly possible for amultitude of various different endpoints to be connected 452, 453.

FIG. 5 is a method diagram illustrating important steps in detecting andanalyzing software exploits or vulnerabilities, according to a preferredaspect of the invention. A file that is targeted for analysis may betranslated into executable binary code 510 by use of a binarytranslation engine 412, and this executable binary code may then betransferred to a sandbox environment 520, 413, for analysis. Thespecific environment in use may vary depending on the code generated bythe binary translation engine 412, including hardware emulators,operating system emulators, and more. The executable binary code is thenexecuted in the simulated environment 530, and the operating system thenmay examine the executing software for any irregularities 540.Irregularities include activities not normally performed by benignsoftware including memory scanning, and deletion of the executablebinary during execution (but the executing code remaining in memory),which are known patterns of malware to avoid detection and elimination.Attempted access of system files, permissions, settings, or networkadapters in suspicious ways may also be classified as “irregularities,”though the characterization and scope of what the operating system 410looks for may grow and change over time as cybersecurity and malwareboth advance. The operating system may also probe the executing softwarefor vulnerabilities and exploits 550, which will often be known forms ofattack, such as the Heartbleed exploit in OPENSSL™, and are known tomany skilled in the art of cybersecurity. The types of vulnerabilitiesand exploits probed for may change and grow as cybersecurity advances asa field. The operating system 410 may then learn new behaviors 560according to the results of analysis, for example the operating system410 may probe for the Heartbleed exploit 550 in a piece of software tosee if it is prone to that exploit, and if it is, the operating systemmay be trained look for similar execution patterns in future softwareanalyses to determine if a piece of software is likely to be vulnerableto the same exploit, an example of reinforcement learning 560. This maybe achieved in numerous ways common in the art including neuralnetworks, a basic decision weight system, and more, all common conceptsin computer science and software development as a whole.

FIG. 6 is a method diagram illustrating the use of advanced endpointinstrumentation to collect data on endpoint devices across a network,according to a preferred aspect. First, the network endpoint must havethe instrumentation installed before it can be utilized 610, and somecommon instrumentations include OSQUERY™ and open source fleetmanagement software including “doorman,” an open source fleet managementsuite for OSQUERY™ Software such as OSQUERY™ allows devices to bequeried and scanned similar to data files or databases, so that propertyand/or relational data about the device may be scanned easily andswiftly by an authorized user, in this case the business operatingsystem 410. Once a form of instrumentation software is installed ontothe endpoints used in the system 610, device data may be remotelyqueried by the business operating system 620, similar to a databasequery over the internet. Device data is then sent back to the machinehosting the business operating system 630, which is then analyzed forpotential vulnerability profiling 640. For example, certain phones thatmay be used in this system have various exploits and vulnerabilitiesthat are exclusive to each other, as do many older operating systems forpersonal computers, and this information would be able to be queried foranalysis 640.

FIG. 7 is a method diagram illustrating the prioritization of softwareflaws and exploits according to a preferred aspect. Vulnerabilities andexploits found in software executed in the sandbox environment 413 arerelayed to the scoring engine 440, 710, which may be either a connectionover a network 430 or a direct physical connection between only the twomachines, or both the scoring engine 440 and operating system 410 may beoperating on the same computing device. The vulnerabilities and exploitsfound in the software execution may then be scored by the scoring engine720, which will assign a higher risk level to exploits which may involvedeleting system critical files, highly evasive code techniques which mayevade most forms of antivirus software, and more, using a scoringmethodology which may be specified and changed at any time by the userof the software. The scoring methodology may be arbitrary or follow anygiven set of rules specified by the user of the software, the importanceof this being that as cybersecurity and malware advance, the need for achanging and advancing ranking of threats is obvious and immediate whatwas considered a horrible computer virus 15 years ago may be easilydetectable today, and similarly, what is considered incredibly high-risktoday may be of no consequence to antivirus software 15 years from now,and therefore be categorized as a low threat to most devices.Regardless, at the time of execution, the scoring engine 440 will theninform the operating system 410 of a ranking of the foundvulnerabilities or threats in the executed software 730, ranking theissues found from most dangerous or pressing, to least dangerous orpressing.

FIG. 8 is a method diagram illustrating the basic steps for patchingexploits and vulnerabilities in analyzed software, according to anaspect. After receiving a list of exploitable or dangerous behaviorsfrom a simulated piece of software 730, the business operating system410 will attempt to make any of various changes or limitationsimplementable at the kernel level to the software's execution 810, to“patch” the vulnerability or threat. Such measures may includetechniques such as Address Space Layout Randomization (ASLR), a memoryprotection process which randomizes the location in computer memorywhere system executable code is loaded, and measures used to patchundesirable behavior or vulnerabilities may include other techniquesincluding data execution prevention (DEP), which prevents certainsectors of memory from being executed, protecting potentially importantsystem processes from attack. There are a large variety of securitymeasures that may be implemented in an effort to patch softwarebehavior, and the importance of noting that it is to patch behavior isthat the system is not analyzing the code itself and re-writing code insoftware to be permanently patched, but rather it is changing systembehavior based on observed software behavior to protect against anybehavior that is either vulnerable to exploitation, or is itselfmalware. If an implemented patch does not solve the undesired behavioror vulnerability in the tested software, and is deemed a failure, thenthe operating system 410 may learn through reinforcement learningalgorithms to try different measures first, if the same behavior occursduring analysis of a different piece of software 830. If the implementedpatch or hotfix does stop the undesirable behavior, and is deemed asuccess, the operating system 410 learns, conversely to the previoussituation, that this patch is an effective measure against suchbehaviors, and will try it sooner than other (either untested orpossibly ineffective) measures that it may have tried 820. This patch orenhancement to the software's functionality is then sent to the endpoint450, 840 which hosted this file or piece of software that was beinganalyzed, so that the undesired behavior or vulnerability is effectivelymitigated.

Hardware Architecture

Generally, the techniques disclosed herein may be implemented onhardware or a combination of software and hardware. For example, theymay be implemented in an operating system kernel, in a separate userprocess, in a library package bound into network applications, on aspecially constructed machine, on an application-specific integratedcircuit (“ASIC”), or on a network interface card.

Software/hardware hybrid implementations of at least some of the aspectsdisclosed herein may be implemented on a programmable network-residentmachine (which should be understood to include intermittently connectednetwork-aware machines) selectively activated or reconfigured by acomputer program stored in memory. Such network devices may havemultiple network interfaces that may be configured or designed toutilize different types of network communication protocols. A generalarchitecture for some of these machines may be described herein in orderto illustrate one or more exemplary means by which a given unit offunctionality may be implemented. According to specific aspects, atleast some of the features or functionalities of the various aspectsdisclosed herein may be implemented on one or more general-purposecomputers associated with one or more networks, such as for example anend-user computer system, a client computer, a network server or otherserver system, a mobile computing device (e.g., tablet computing device,mobile phone, smartphone, laptop, or other appropriate computingdevice), a consumer electronic device, a music player, or any othersuitable electronic device, router, switch, or other suitable device, orany combination thereof. In at least some aspects, at least some of thefeatures or functionalities of the various aspects disclosed herein maybe implemented in one or more virtualized computing environments (e.g.,network computing clouds, virtual machines hosted on one or morephysical computing machines, or other appropriate virtual environments).

Referring now to FIG. 9 , there is shown a block diagram depicting anexemplary computing device 10 suitable for implementing at least aportion of the features or functionalities disclosed herein. Computingdevice 10 may be, for example, any one of the computing machines listedin the previous paragraph, or indeed any other electronic device capableof executing software- or hardware-based instructions according to oneor more programs stored in memory. Computing device 10 may be configuredto communicate with a plurality of other computing devices, such asclients or servers, over communications networks such as a wide areanetwork a metropolitan area network, a local area network, a wirelessnetwork, the Internet, or any other network, using known protocols forsuch communication, whether wireless or wired.

In one embodiment, computing device 10 includes one or more centralprocessing units (CPU) 12, one or more interfaces 15, and one or morebusses 14 (such as a peripheral component interconnect (PCI) bus). Whenacting under the control of appropriate software or firmware, CPU 12 maybe responsible for implementing specific functions associated with thefunctions of a specifically configured computing device or machine. Forexample, in at least one embodiment, a computing device 10 may beconfigured or designed to function as a server system utilizing CPU 12,local memory 11 and/or remote memory 16, and interface(s) 15. In atleast one embodiment, CPU 12 may be caused to perform one or more of thedifferent types of functions and/or operations under the control ofsoftware modules or components, which for example, may include anoperating system and any appropriate applications software, drivers, andthe like.

CPU 12 may include one or more processors 13 such as, for example, aprocessor from one of the Intel, ARM, Qualcomm, and AMD families ofmicroprocessors. In some embodiments, processors 13 may includespecially designed hardware such as application-specific integratedcircuits (ASICs), electrically erasable programmable read-only memories(EEPROMs), field-programmable gate arrays (FPGAs), and so forth, forcontrolling operations of computing device 10. In a specific embodiment,a local memory 11 (such as non-volatile random access memory (RAM)and/or read-only memory (ROM), including for example one or more levelsof cached memory) may also form part of CPU 12. However, there are manydifferent ways in which memory may be coupled to system 10. Memory 11may be used for a variety of purposes such as, for example, cachingand/or storing data, programming instructions, and the like. It shouldbe further appreciated that CPU 12 may be one of a variety ofsystem-on-a-chip (SOC) type hardware that may include additionalhardware such as memory or graphics processing chips, such as a QUALCOMMSNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly commonin the art, such as for use in mobile devices or integrated devices.

As used herein, the term “processor” is not limited merely to thoseintegrated circuits referred to in the art as a processor, a mobileprocessor, or a microprocessor, but broadly refers to a microcontroller,a microcomputer, a programmable logic controller, anapplication-specific integrated circuit, and any other programmablecircuit.

In one embodiment, interfaces 15 are provided as network interface cards(NICs). Generally, NICs control the sending and receiving of datapackets over a computer network; other types of interfaces 15 may forexample support other peripherals used with computing device 10. Amongthe interfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces,graphics interfaces, and the like. In addition, various types ofinterfaces may be provided such as, for example, universal serial bus(USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radiofrequency (RF), BLUETOOTH™, near-field communications (e.g., usingnear-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fastEthernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) orexternal SATA (ESATA) interfaces, high-definition multimedia interface(HDMI), digital visual interface (DVI), analog or digital audiointerfaces, asynchronous transfer mode (ATM) interfaces, high-speedserial interface (HSSI) interfaces, Point of Sale (POS) interfaces,fiber data distributed interfaces (FDDIs), and the like. Generally, suchinterfaces 15 may include physical ports appropriate for communicationwith appropriate media. In some cases, they may also include anindependent processor (such as a dedicated audio or video processor, asis common in the art for high-fidelity A/V hardware interfaces) and, insome instances, volatile and/or non-volatile memory (e.g., RAM).

Although the system shown in FIG. 9 illustrates one specificarchitecture for a computing device 10 for implementing one or more ofthe inventions described herein, it is by no means the only devicearchitecture on which at least a portion of the features and techniquesdescribed herein may be implemented. For example, architectures havingone or any number of processors 13 may be used, and such processors 13may be present in a single device or distributed among any number ofdevices. In one embodiment, a single processor 13 handles communicationsas well as routing computations, while in other embodiments a separatededicated communications processor may be provided. In variousembodiments, different types of features or functionalities may beimplemented in a system according to the invention that includes aclient device (such as a tablet device or smartphone running clientsoftware) and server systems (such as a server system described in moredetail below).

Regardless of network device configuration, the system of the presentinvention may employ one or more memories or memory modules (such as,for example, remote memory block 16 and local memory 11) configured tostore data, program instructions for the general-purpose networkoperations, or other information relating to the functionality of theembodiments described herein (or any combinations of the above). Programinstructions may control execution of or comprise an operating systemand/or one or more applications, for example. Memory 16 or memories 11,16 may also be configured to store data structures, configuration data,encryption data, historical system operations information, or any otherspecific or generic non-program information described herein.

Because such information and program instructions may be employed toimplement one or more systems or methods described herein, at least somenetwork device embodiments may include nontransitory machine-readablestorage media, which, for example, may be configured or designed tostore program instructions, state information, and the like forperforming various operations described herein. Examples of suchnontransitory machine-readable storage media include, but are notlimited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks; magneto-optical mediasuch as optical disks, and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory devices (ROM), flash memory (as is common in mobile devices andintegrated systems), solid state drives (SSD) and “hybrid SSD” storagedrives that may combine physical components of solid state and hard diskdrives in a single hardware device (as are becoming increasingly commonin the art with regard to personal computers), memristor memory, randomaccess memory (RAM), and the like. It should be appreciated that suchstorage means may be integral and non-removable (such as RAM hardwaremodules that may be soldered onto a motherboard or otherwise integratedinto an electronic device), or they may be removable such as swappableflash memory modules (such as “thumb drives” or other removable mediadesigned for rapidly exchanging physical storage devices),“hot-swappable” hard disk drives or solid state drives, removableoptical storage discs, or other such removable media, and that suchintegral and removable storage media may be utilized interchangeably.Examples of program instructions include both object code, such as maybe produced by a compiler, machine code, such as may be produced by anassembler or a linker, byte code, such as may be generated by forexample a JAVA™ compiler and may be executed using a Java virtualmachine or equivalent, or files containing higher level code that may beexecuted by the computer using an interpreter (for example, scriptswritten in Python, Perl, Ruby, Groovy, or any other scripting language).

In some embodiments, systems according to the present invention may beimplemented on a standalone computing system. Referring now to FIG. 10 ,there is shown a block diagram depicting a typical exemplaryarchitecture of one or more embodiments or components thereof on astandalone computing system. Computing device 20 includes processors 21that may run software that carry out one or more functions orapplications of embodiments of the invention, such as for example aclient application 24. Processors 21 may carry out computinginstructions under control of an operating system 22 such as, forexample, a version of MICROSOFT WINDOWS™ operating system, APPLE OSX™ oriOS™ operating systems, some variety of the Linux operating system,ANDROID™ operating system, or the like. In many cases, one or moreshared services 23 may be operable in system 20, and may be useful forproviding common services to client applications 24. Services 23 may forexample be WINDOWS™ services, user-space common services in a Linuxenvironment, or any other type of common service architecture used withoperating system 21. Input devices 28 may be of any type suitable forreceiving user input, including for example a keyboard, touchscreen,microphone (for example, for voice input), mouse, touchpad, trackball,or any combination thereof. Output devices 27 may be of any typesuitable for providing output to one or more users, whether remote orlocal to system 20, and may include for example one or more screens forvisual output, speakers, printers, or any combination thereof. Memory 25may be random-access memory having any structure and architecture knownin the art, for use by processors 21, for example to run software.Storage devices 26 may be any magnetic, optical, mechanical, memristor,or electrical storage device for storage of data in digital form (suchas those described above, referring to FIG. 9 ). Examples of storagedevices 26 include flash memory, magnetic hard drive, CD-ROM, and/or thelike.

In some embodiments, systems of the present invention may be implementedon a distributed computing network, such as one having any number ofclients and/or servers. Referring now to FIG. 11 , there is shown ablock diagram depicting an exemplary architecture 30 for implementing atleast a portion of a system according to an embodiment of the inventionon a distributed computing network. According to the embodiment, anynumber of clients 33 may be provided. Each client 33 may run softwarefor implementing client-side portions of the present invention; clientsmay comprise a system 20 such as that illustrated in FIG. 10 . Inaddition, any number of servers 32 may be provided for handling requestsreceived from one or more clients 33. Clients 33 and servers 32 maycommunicate with one another via one or more electronic networks 31,which may be in various embodiments any of the Internet, a wide areanetwork, a mobile telephony network (such as CDMA or GSM cellularnetworks), a wireless network (such as WiFi, WiMAX, LTE, and so forth),or a local area network (or indeed any network topology known in theart; the invention does not prefer any one network topology over anyother). Networks 31 may be implemented using any known networkprotocols, including for example wired and/or wireless protocols.

In addition, in some embodiments, servers 32 may call external services37 when needed to obtain additional information, or to refer toadditional data concerning a particular call. Communications withexternal services 37 may take place, for example, via one or morenetworks 31. In various embodiments, external services 37 may compriseweb-enabled services or functionality related to or installed on thehardware device itself. For example, in an embodiment where clientapplications 24 are implemented on a smartphone or other electronicdevice, client applications 24 may obtain information stored in a serversystem 32 in the cloud or on an external service 37 deployed on one ormore of a particular enterprise's or user's premises.

In some embodiments of the invention, clients 33 or servers 32 (or both)may make use of one or more specialized services or appliances that maybe deployed locally or remotely across one or more networks 31. Forexample, one or more databases 34 may be used or referred to by one ormore embodiments of the invention. It should be understood by one havingordinary skill in the art that databases 34 may be arranged in a widevariety of architectures and using a wide variety of data access andmanipulation means. For example, in various embodiments one or moredatabases 34 may comprise a relational database system using astructured query language (SQL), while others may comprise analternative data storage technology such as those referred to in the artas “NoSQL” (for example, HADOOP CASSANDRA™, GOOGLE BIGTABLE™, and soforth). In some embodiments, variant database architectures such ascolumn-oriented databases, in-memory databases, clustered databases,distributed databases, or even flat file data repositories may be usedaccording to the invention. It will be appreciated by one havingordinary skill in the art that any combination of known or futuredatabase technologies may be used as appropriate, unless a specificdatabase technology or a specific arrangement of components is specifiedfor a particular embodiment herein. Moreover, it should be appreciatedthat the term “database” as used herein may refer to a physical databasemachine, a cluster of machines acting as a single database system, or alogical database within an overall database management system. Unless aspecific meaning is specified for a given use of the term “database”, itshould be construed to mean any of these senses of the word, all ofwhich are understood as a plain meaning of the term “database” by thosehaving ordinary skill in the art.

Similarly, most embodiments of the invention may make use of one or moresecurity systems 36 and configuration systems 35. Security andconfiguration management are common information technology (IT) and webfunctions, and some amount of each are generally associated with any ITor web systems. It should be understood by one having ordinary skill inthe art that any configuration or security subsystems known in the artnow or in the future may be used in conjunction with embodiments of theinvention without limitation, unless a specific security 36 orconfiguration system 35 or approach is specifically required by thedescription of any specific embodiment.

FIG. 12 shows an exemplary overview of a computer system 40 as may beused in any of the various locations throughout the system. It isexemplary of any computer that may execute code to process data. Variousmodifications and changes may be made to computer system 40 withoutdeparting from the broader scope of the system and method disclosedherein. Central processor unit (CPU) 41 is connected to bus 42, to whichbus is also connected memory 43, nonvolatile memory 44, display 47,input/output (I/O) unit 48, and network interface card (NIC) 53. I/Ounit 48 may, typically, be connected to keyboard 49, pointing device 50,hard disk 52, and real-time clock 51. NIC 53 connects to network 54,which may be the Internet or a local network, which local network may ormay not have connections to the Internet. Also shown as part of system40 is power supply unit 45 connected, in this example, to a mainalternating current (AC) supply 46. Not shown are batteries that couldbe present, and many other devices and modifications that are well knownbut are not applicable to the specific novel functions of the currentsystem and method disclosed herein. It should be appreciated that someor all components illustrated may be combined, such as in variousintegrated applications, for example Qualcomm or Samsungsystem-on-a-chip (SOC) devices, or whenever it may be appropriate tocombine multiple capabilities or functions into a single hardware device(for instance, in mobile devices such as smartphones, video gameconsoles, in-vehicle computer systems such as navigation or multimediasystems in automobiles, or other integrated hardware devices).

In various embodiments, functionality for implementing systems ormethods of the present invention may be distributed among any number ofclient and/or server components. For example, various software modulesmay be implemented for performing various functions in connection withthe present invention, and such modules may be variously implemented torun on server and/or client components.

The skilled person will be aware of a range of possible modifications ofthe various embodiments described above. Accordingly, the presentinvention is defined by the claims and their equivalents.

What is claimed is:
 1. A system for sandboxed malware analysis andautomated vulnerability protection, comprising: a computing devicecomprising a memory and a processor; a business operating systemcomprising a first plurality of programming instructions stored in thememory of, and operating on the processor of, the computing device,wherein the first plurality of programming instructions, when operatingon the processor, cause the computing device to: intercept a filecomprising executable machine code at a network device endpoint;identify a type of device on which the executable machine code willoperate; transfer the executable machine code to a device-specificsandbox environment, the device-specific sandbox environment comprisinga safe environment that emulates functionality of the identified type ofdevice where malware is unable to affect systems outside of thedevice-specific sandbox environment; receive an identified vulnerabilityfrom the sandbox environment related to the identified type of device;and change an operational behavior of a real device of the identifiedtype of device to prevent exploitation of the identified vulnerabilitythrough either address space layout randomization or data executionprevention; and a device-specific sandbox environment comprising asecond plurality of programming instructions stored in the memory of,and operating on the processor of, the computing device, wherein thesecond plurality of programming instructions, when operating on theprocessor, cause the computing device to: receive the executable machinecode from the business operating system; execute the executable machinecode on an emulator of the identified type of device; identify anirregularity in the execution of the executable machine code on theemulator, the irregularity comprising two or more of the followingactivities performed in suspicious ways not normally performed by benignsoftware: memory scanning, deletion of the file containing theexecutable machine code from storage media, access of system files,access of permissions, access of security settings, and access ofnetwork adapters; identify a vulnerability of the identified type ofdevice being targeted by the identified irregularity in the execution ofthe executable machine code; and send the identified vulnerability tothe business operating system.
 2. A method for sandboxed malwareanalysis and automated vulnerability protection, comprising the stepsof: intercepting a file comprising executable machine code at a networkdevice endpoint; identifying a type of device on which the executablemachine code will operate; transferring the executable machine code to adevice-specific sandbox environment, the device-specific sandboxenvironment comprising a safe environment that emulates functionality ofthe identified type of device where malware is unable to affect systemsoutside of the device-specific sandbox environment; executing theexecutable machine code on an emulator for the identified type of devicewithin the device-specific sandbox environment; identifying anirregularity in the execution of the executable machine code on theemulator within the device-specific sandbox environment, theirregularity comprising two or more of the following activitiesperformed in suspicious ways not normally performed by benign software:memory scanning, deletion of the file containing the executable machinecode from storage media, access of system files, access of permissions,access of security settings, and access of network adapters; identifyinga vulnerability of the identified type of device being targeted by theidentified irregularity in the execution of the executable machine code;and changing an operational behavior of a real device of the identifiedtype of device to prevent exploitation of the identified vulnerabilitythrough either address space layout randomization or data executionprevention.